"""simple docstring"""



def 	_lowerCamelCase(	a				):
       __a       =    [int(a				) for i in ip_va_address.split("."				) if i.isdigit()]
       return len(a				) == 4 and all(0 <= int(a				) <= 2_5_4 for octet in octets				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:str									=			input().strip()
   SCREAMING_SNAKE_CASE__:Union[str, Any]									=			"""valid""" if is_ip_va_address_valid(ip) else """invalid"""
   print(F'''{ip} is a {valid_or_invalid} IP v4 address.''')

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
